if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("rtracklayer")
install.packages("BiocManager")
BiocManager::install("rtracklayer")
library(rtracklayer)
BiocManager::install("rtracklayer")
BiocManager::install("rtracklayer")
BiocManager::install("Rhtslib")
install.packages("Rserve")
install.packages("jsonlite")
library('rtracklayer', quietly = TRUE)
library('rtracklayer', quietly = TRUE, warn.conflicts = FALSE)
library('rtracklayer', quietly = TRUE, warn.conflicts = FALSE)
chain = import.chain('Projects/BIM9/Sfari_Gene_CNV/scripts/R/liftover/chain/hg18ToHg38.over.chain')
package_version('Rserve')
package_version("Rserve")
packageVersion("Rserve")
install.packages("Rserve", "Rserve_1.8-6.tgz", "http://www.rforge.net/")
install.packages("Rserve", "Rserve_1.8-6.tgz", "http://www.rforge.net/")
install.packages("Rserve", "Rserve_1.8-6.tgz", "http://www.rforge.net/")
remove.packages('Rserve')
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
library('Rserve')
packageVersion('Rserve')
library(rtracklayer)
chain = import.chain('/home/afs/Projects/BIM9/Sfari_Gene_CNV/scripts/R/liftover/chain/hg18ToHg38.over.chain')
library(Rserve)
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
packageVersion('Rserve')
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
install.packages('Rserve',,"http://rforge.net/",type="source")
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
library(rtracklayer)
library(rtracklayer)
chain = import.chain('Projects/BIM9/Sfari_Gene_CNV/scripts/R/liftover/chain/hg16ToHg38.over.chain')
View(chain)
class(chain)
chain = import.chain('Projects/BIM9/Sfari_Gene_CNV/scripts/R/liftover/chain/hg18ToHg38.over.chain')
class(chain)
packageVersion("Rserve")
packageVersion("Rserve")
remove.packages("Rserve", lib="/usr/lib/R/site-library")
packageVersion('methods')
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
versio()
help()
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
install.packages("~/Downloads/Rserve_1.8-6.tar.gz", repos = NULL, type = "source")
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
install.packages("~/Downloads/Rserve_1.7.tar.gz", repos = NULL, type = "source")
install.packages("~/Downloads/Rserve_1.7.tar.gz", repos = NULL, type = "source")
install.packages("~/Downloads/Rserve_1.7.tar.gz", repos = NULL, type = "source")
install.packages("~/Downloads/Rserve_1.7.tar.gz", repos = NULL, type = "source")
install.packages("~/Downloads/Rserve_1.8-5.tar.gz", repos = NULL, type = "source")
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
install.packages("Rserve")
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
install.packages("Rserve",, "http://www.rforge.net/")
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
install.packages("~/Downloads/Rserve_1.8-6.tar.gz", repos = NULL, type = "source")
sessionInfo()
library('Rserve')
install.packages("Rserve",,"http://rforge.net")
library(Rserve)
Rserve()
Rserve('--no-save')
Rserve(args = '--no-save')
install.packages("Rserve",,"http://rforge.net")
library(Rserve)
Rserve(args = '--no-save')
q()
packageVersion('Rserve')
.libPaths()
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
install.packages("Rserve",,"http://rforge.net")
remove.packages("Rserve", lib="~/R/x86_64-pc-linux-gnu-library/3.6")
install.packages("Rserve",,"http://rforge.net")
library(Rserve)
Rserve()
Rserve(args = '--no-save')
BiocManager::install("biomaRt")
library('biomaRt')
listMarts()
ensembl = useMart("ensembl",dataset="hsapiens_gene_ensembl")
listFilters()
listFilters(ensembl)
listFilters(ensembl)[1:5,]
filters = listFilters(ensembl)
View(filters)
View(filters)
attributes = listAttributes(ensembl)
attributes[1:5,]
attributes[1:10,]
attributes[10:20,]
attributes[20:30,]
BiocManager::install("GoSemSim")
library('GOSemSim')
library(org.Hs.eg.db)
BiocManager::install("org.Hs.eg.db")
library(org.Hs.eg.db)
hsGO = godata(org.Hs.eg.db)
hsGO = godata(org.Hs.eg.db, ont = 'BP')
names(hsGO$IC)
names(hsGO@IC)
hsGO@IC['GO:0007399']
hsGO@IC['GO:0021816']
hsGO = godata(org.Hs.eg.db)
library(org.Hs.eg.db)
hsGO = godata(org.Hs.egGO)
hsGO = org.Hs.egGO
hsGO = godata(org.Hs.eg.db)
hsGO@IC['GO:0021816']
hsGO = org.Hs.egGO
library('GOSemSim')
hsGO = godata(org.Hs.eg.db)
hsGO = godata(org.Hs.eg.db, ont = 'CC')
hsGO@IC['GO:0005737']
hsGO@IC['GO:0000118']
q()
install.packages("outliers")
Sys.getenv("R_USER")
Sys.getenv("HOME")
data(chemdiab)
install.packages("locfit")
require('locfit')
data(chemdiab)
View(chemdiab)
data = data(chemdiab)
View(chemdiab)
d = data(chemdiab)
d
chemdiab
require('locfit')
data(chemdiab)
chemdiab
install.packages("tidyverse")
library(tidyverse)
library(lubridate)
installed.packages()
1_demo = 2
library("tidyverse")
package.install(tydiverse)
install.packages(tidyverse)
install.packages("tidyverse")
install.packages("tidyverse")
install.packages("skimr")
install.packages("janitor")
library(tidyverse)
library(skimr)
library(janitor)
install.packages("janitor")
install.packages("skimr")
install.packages("skimr")
install.packages("janitor")
library(tidyverse)
library(skimr)
library(janitor)
bookings_df <- read_csv("hotel_bookings.csv")
bookings_df <- read_csv("Downloads/hotel_bookings.csv")
bookings_df <- read_csv("hotel_bookings.csv")
bookings_df <- read_csv("hotel_bookings.csv")
bookings_df <- read_csv("Downloads/hotel_bookings.csv")
head(bookings_df)
str(bookings_df)
skim_without_charts(bookings_df)
trimmed_df <- bookings_df %>%
select( , , )
View(trimmed_df)
trimmed_df %>%
select(hotel, is_canceled, lead_time) %>%
rename( = hotel)
trimmed_df %>%
select(hotel, is_canceled, lead_time) %>%
rename(hotel_type = hotel)
trimmed_df <- bookings_df %>%
select(hotel, is_canceled, lead_time)
trimmed_df %>%
select(hotel, is_canceled, lead_time) %>%
rename(hotel_type = hotel)
example_df <- bookings_df %>%
select(arrival_date_year, arrival_date_month) %>%
unite(arrival_month_year, c("arrival_date_month", "arrival_date_year"), sep = " ")
View(example_df)
View(bookings_df)
example_df <- bookings_df %>%
mutate(guests = children + babies + adults)
head(example_df)
View(example_df)
example_df <- bookings_df %>%
head(example_df)
View(bookings_df)
example_df <- bookings_df %>%
summarize(number_cancled = sum(is_canceled), average_lead_time = mean(lead_time))
head(example_df)
?rename
?unite
install.packages('ggplot2')
install.packages('palmerpenguins')
library(ggplot2)
library(palmerpenguins)
data(penguins)
View(penguins)
ggplot(data = penguins) +
geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g))
hotel_bookings <- read.csv("hotel_bookings.csv")
head(hotel_bookings)
install.packages('ggplot2')
library(ggplot2)
#install.packages('ggplot2')
library(ggplot2)
ggplot(data = hotel_bookings) +
geom_point(mapping = aes(x = lead_time, y = children))
ggplot(data = hotel_bookings) +
geom_point(mapping = aes(x = stays_in_weekend_nights, y = children))
hotel_bookings <- read.csv("hotel_bookings.csv")
hotel_bookings <- read.csv("hotel_bookings.csv")
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel))
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel, 'fill=deposit_type'))
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel, fill=deposit_type))
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel, fill=market_segment))
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel)) +
facet_wrap(~deposit_type)
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel)) +
facet_wrap(~deposit_type) +
theme(axis.text.x = element_text(angle = 45))
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel)) +
facet_grid(~deposit_type) +
theme(axis.text.x = element_text(angle = 45))
hotel_bookings <- read.csv("hotel_bookings.csv")
ggplot(data = hotel_bookings) +
geom_point(mapping = aes(x = lead_time, y = children))
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = hotel, fill = market_segment))
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = hotel)) +
facet_wrap(~market_segment)
#install.packages('tidyverse')
library(tidyverse)
onlineta_city_hotels <- filter(hotel_bookings,
(hotel=="City Hotel" &
hotel_bookings$market_segment=="Online TA"))
View(onlineta_city_hotels)
ggplot(data = onlineta_city_hotels) +
geom_point(mapping = aes(x = lead_time, y = children))
View(onlineta_city_hotels)
install.packages("rmarkdown")
install.packages("rmarkdown")
knit_with_parameters('/home/afs/Projects/GoogleDataAnalytics/07_programming_r/lesson4_annotations.Rmd')
?rename
library(tidyverse)
?rename
?select
?sd
?summarize
?filter
setwd('Projects/BIM9/Sfari_Gene_CNV/scripts/graph_db/node_classification/model_metrics/')
# Read the CSV file into a data frame
data <- read.csv("DopamineGeneticSimilarity_centrality.csv")
# Specify the columns you want to calculate statistics for
columns_to_calculate <- c("accuracy", "f1", "precision_asd", "precision_dd", "recall_asd", "recall_dd")
# Calculate the mean for the specified columns
means <- colMeans(data[columns_to_calculate])
# Calculate the standard deviation for the specified columns
sds <- apply(data[columns_to_calculate], 2, sd)
# Create a data frame to store the results
result_df <- data.frame(Column = columns_to_calculate, Mean = means, SD = sds)
# Print the result data frame
print(result_df)
# Read the CSV file into a data frame
data <- read.csv("DopamineGoSimilarity_centrality.csv")
# Specify the columns you want to calculate statistics for
columns_to_calculate <- c("accuracy", "f1", "precision_asd", "precision_dd", "recall_asd", "recall_dd")
# Calculate the mean for the specified columns
means <- colMeans(data[columns_to_calculate])
# Calculate the standard deviation for the specified columns
sds <- apply(data[columns_to_calculate], 2, sd)
# Create a data frame to store the results
result_df <- data.frame(Column = columns_to_calculate, Mean = means, SD = sds)
# Print the result data frame
print(result_df)
# Read the CSV file into a data frame
data <- read.csv("DopamineGeneticGoSimilarity_centrality.csv")
# Specify the columns you want to calculate statistics for
columns_to_calculate <- c("accuracy", "f1", "precision_asd", "precision_dd", "recall_asd", "recall_dd")
# Calculate the mean for the specified columns
means <- colMeans(data[columns_to_calculate])
# Calculate the standard deviation for the specified columns
sds <- apply(data[columns_to_calculate], 2, sd)
# Create a data frame to store the results
result_df <- data.frame(Column = columns_to_calculate, Mean = means, SD = sds)
# Print the result data frame
print(result_df)
# Read the CSV file into a data frame
data <- read.csv("DopamineGeneticSimilarity_embeddings.csv")
# Specify the columns you want to calculate statistics for
columns_to_calculate <- c("accuracy", "f1", "precision_asd", "precision_dd", "recall_asd", "recall_dd")
# Calculate the mean for the specified columns
means <- colMeans(data[columns_to_calculate])
# Calculate the standard deviation for the specified columns
sds <- apply(data[columns_to_calculate], 2, sd)
# Create a data frame to store the results
result_df <- data.frame(Column = columns_to_calculate, Mean = means, SD = sds)
# Print the result data frame
print(result_df)
# Read the CSV file into a data frame
data <- read.csv("DopamineGoSimilarity_embeddings.csv")
# Specify the columns you want to calculate statistics for
columns_to_calculate <- c("accuracy", "f1", "precision_asd", "precision_dd", "recall_asd", "recall_dd")
# Calculate the mean for the specified columns
means <- colMeans(data[columns_to_calculate])
# Calculate the standard deviation for the specified columns
sds <- apply(data[columns_to_calculate], 2, sd)
# Create a data frame to store the results
result_df <- data.frame(Column = columns_to_calculate, Mean = means, SD = sds)
# Print the result data frame
print(result_df)
# Read the CSV file into a data frame
data <- read.csv("DopamineGeneticGoSimilarity_embeddings.csv")
# Specify the columns you want to calculate statistics for
columns_to_calculate <- c("accuracy", "f1", "precision_asd", "precision_dd", "recall_asd", "recall_dd")
# Calculate the mean for the specified columns
means <- colMeans(data[columns_to_calculate])
# Calculate the standard deviation for the specified columns
sds <- apply(data[columns_to_calculate], 2, sd)
# Create a data frame to store the results
result_df <- data.frame(Column = columns_to_calculate, Mean = means, SD = sds)
# Print the result data frame
print(result_df)
